Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

tissue_of_interest = "Liver"

Load the requisite packages and some additional helper functions.

library(here)
## here() starts at /Users/olgabot/code/tabula-muris
library(useful)
## Loading required package: ggplot2
library(Seurat)
## Loading required package: cowplot
## 
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggplot2':
## 
##     ggsave
## Loading required package: Matrix
## Warning: namespace 'Biobase' is not available and has been replaced
## by .GlobalEnv when processing object 'call.'
## Warning: namespace 'lme4' is not available and has been replaced
## by .GlobalEnv when processing object 'call.'
## Warning: namespace 'MatrixModels' is not available and has been replaced
## by .GlobalEnv when processing object 'call.'
## Warning: namespace 'Biobase' is not available and has been replaced
## by .GlobalEnv when processing object 'call.'
## Warning: namespace 'lme4' is not available and has been replaced
## by .GlobalEnv when processing object 'call.'
## Warning: namespace 'MatrixModels' is not available and has been replaced
## by .GlobalEnv when processing object 'call.'
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.4.2
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(Matrix)

save_dir = here('00_data_ingest', 'tissue_robj')
# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', 'facs_raw_data', 'metadata_FACS.csv')

plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadata
##     plate.barcode      mouse.id          tissue           subtissue
## 1         D041914         3_8_M         Bladder                <NA>
## 2         D042253         3_9_M         Bladder                <NA>
## 3       MAA000487        3_10_M         Bladder                <NA>
## 4         B000610        3_56_F         Bladder                <NA>
## 5         B002764        3_38_F         Bladder                <NA>
## 6         B002771        3_39_F         Bladder                <NA>
## 7       MAA000538         3_8_M   Brain_Neurons          Cerebellum
## 8       MAA000550        3_10_M   Brain_Neurons         Hippocampus
## 9       MAA000553        3_10_M   Brain_Neurons         Hippocampus
## 10      MAA000560        3_10_M   Brain_Neurons              Cortex
## 11      MAA000561        3_10_M   Brain_Neurons              Cortex
## 12      MAA000563        3_10_M   Brain_Neurons         Hippocampus
## 13      MAA000564        3_10_M   Brain_Neurons            Striatum
## 14      MAA000570         3_8_M Brain_Microglia          Cerebellum
## 15      MAA000571         3_9_M Brain_Microglia          Cerebellum
## 16      MAA000572         3_8_M Brain_Microglia              Cortex
## 17      MAA000573         3_9_M Brain_Microglia              Cortex
## 18      MAA000578        3_10_M   Brain_Neurons          Cerebellum
## 19      MAA000581        3_10_M   Brain_Neurons          Cerebellum
## 20      MAA000590         3_9_M Brain_Microglia            Striatum
## 21      MAA000591         3_8_M Brain_Microglia         Hippocampus
## 22      MAA000592         3_9_M Brain_Microglia         Hippocampus
## 23      MAA000593         3_8_M Brain_Microglia            Striatum
## 24      MAA000603        3_10_M Brain_Microglia         Hippocampus
## 25      MAA000604        3_10_M Brain_Microglia              Cortex
## 26      MAA000605        3_10_M Brain_Microglia          Cerebellum
## 27      MAA000615        3_10_M   Brain_Neurons            Striatum
## 28      MAA000617        3_10_M Brain_Microglia            Striatum
## 29      MAA000638         3_9_M   Brain_Neurons          Cerebellum
## 30      MAA000641         3_9_M   Brain_Neurons          Cerebellum
## 31      MAA000902        3_11_M   Brain_Neurons            Striatum
## 32      MAA000923         3_9_M   Brain_Neurons         Hippocampus
## 33      MAA000924         3_8_M   Brain_Neurons          Cerebellum
## 34      MAA000925         3_9_M   Brain_Neurons            Striatum
## 35      MAA000926         3_9_M   Brain_Neurons              Cortex
## 36      MAA000928         3_9_M   Brain_Neurons          Cerebellum
## 37      MAA000930         3_8_M   Brain_Neurons              Cortex
## 38      MAA000932        3_11_M   Brain_Neurons         Hippocampus
## 39      MAA000935         3_8_M   Brain_Neurons         Hippocampus
## 40      MAA000940         3_8_M   Brain_Neurons            Striatum
## 41      MAA000941         3_8_M   Brain_Neurons         Hippocampus
## 42      MAA000942         3_8_M   Brain_Neurons            Striatum
## 43      MAA000944         3_9_M   Brain_Neurons              Cortex
## 44      MAA000947         3_9_M   Brain_Neurons         Hippocampus
## 45      MAA000424        3_11_M   Brain_Neurons          Cerebellum
## 46      MAA001845        3_39_F   Brain_Neurons          Cerebellum
## 47      MAA001853        3_38_F   Brain_Neurons          Cerebellum
## 48      MAA001854        3_38_F   Brain_Neurons          Cerebellum
## 49      MAA001856        3_39_F   Brain_Neurons          Cerebellum
## 50      MAA001871        3_39_F           Colon            Proximal
## 51      MAA001894        3_39_F   Brain_Neurons              Cortex
## 52      MAA001895        3_38_F   Brain_Neurons              Cortex
## 53        B000621        3_56_F   Brain_Neurons         Hippocampus
## 54        B000825        3_38_F Brain_Microglia            Striatum
## 55        B000826        3_39_F Brain_Microglia              Cortex
## 56        B003274        3_38_F   Brain_Neurons            Striatum
## 57        B003275        3_38_F   Brain_Neurons         Hippocampus
## 58        B003277        3_39_F   Brain_Neurons         Hippocampus
## 59        B003278        3_38_F Brain_Microglia              Cortex
## 60        B003279        3_38_F Brain_Microglia          Cerebellum
## 61        B003281        3_39_F Brain_Microglia         Hippocampus
## 62        B003290        3_38_F   Brain_Neurons            Striatum
## 63        B003292        3_39_F Brain_Microglia            Striatum
## 64        B001688        3_38_F   Brain_Neurons         Hippocampus
## 65        B001689        3_38_F Brain_Microglia         Hippocampus
## 66        B000404        3_56_F   Brain_Neurons          Cerebellum
## 67        B003728        3_56_F   Brain_Neurons            Striatum
## 68        B001176        3_56_F Brain_Microglia            Striatum
## 69      MAA000583         3_9_M           Colon              Distal
## 70      MAA000611         3_8_M           Colon            Proximal
## 71      MAA000612         3_9_M           Colon            Proximal
## 72      MAA000870        3_10_M           Colon              Distal
## 73      MAA000871        3_11_M           Colon            Proximal
## 74      MAA000909        3_10_M           Colon            Proximal
## 75      MAA000937         3_8_M           Colon              Distal
## 76      MAA001539        3_56_F           Colon              Distal
## 77      MAA001632        3_56_F           Colon            Proximal
## 78      MAA001869        3_38_F           Colon            Proximal
## 79      MAA001872        3_39_F           Colon            Proximal
## 80      MAA001873        3_38_F           Colon              Distal
## 81      MAA001875        3_39_F           Colon              Distal
## 82      MAA000385        3_10_M             Fat                SCAT
## 83      MAA000386        3_11_M             Fat                SCAT
## 84      MAA000388        3_11_M             Fat                 GAT
## 85      MAA000439        3_10_M             Fat                 GAT
## 86      MAA000441        3_10_M             Fat                 BAT
## 87      MAA000442        3_11_M             Fat                 BAT
## 88      MAA000510         3_8_M             Fat                SCAT
## 89      MAA000554         3_8_M             Fat                 GAT
## 90      MAA000556         3_9_M             Fat                 BAT
## 91      MAA000599         3_8_M             Fat                 BAT
## 92      MAA000873        3_10_M             Fat                 MAT
## 93      MAA000877        3_11_M             Fat                 MAT
## 94      MAA000913         3_9_M             Fat                 GAT
## 95      MAA000914         3_9_M             Fat                 MAT
## 96      MAA000934         3_9_M             Fat                SCAT
## 97      MAA000531         3_8_M             Fat                 MAT
## 98        D043520        3_39_F             Fat                 MAT
## 99        D043526        3_39_F             Fat                SCAT
## 100       D045058        3_39_F             Fat                 GAT
## 101       D045064        3_38_F             Fat                 MAT
## 102       D045067        3_38_F             Fat                 GAT
## 103       B000127        3_38_F             Fat                SCAT
## 104       B001216        3_38_F             Fat                 BAT
## 105       B002707        3_39_F             Fat                 BAT
## 106       B001960        3_56_F             Fat                 BAT
## 107       B002314        3_56_F             Fat                 MAT
## 108       B002954        3_56_F             Fat                SCAT
## 109       B002955        3_56_F             Fat                 GAT
## 110     MAA000398         3_9_M           Heart                  LA
## 111     MAA000399         3_9_M           Heart                  RV
## 112     MAA000400         3_8_M           Heart                  LA
## 113     MAA000452         3_8_M           Heart                  RV
## 114     MAA000586         3_8_M           Heart                  RA
## 115     MAA000587         3_8_M           Heart                  LV
## 116     MAA000589         3_9_M           Heart                  LV
## 117     MAA000594         3_8_M           Heart               Aorta
## 118     MAA000595         3_9_M           Heart               Aorta
## 119     MAA000898        3_11_M           Heart                  RV
## 120     MAA000899        3_10_M           Heart                  RA
## 121     MAA000901        3_10_M           Heart                  RV
## 122     MAA000903        3_11_M           Heart                  RA
## 123     MAA000906        3_11_M           Heart               Aorta
## 124     MAA000908        3_10_M           Heart               Aorta
## 125     MAA000917        3_11_M           Heart                  LV
## 126     MAA000918        3_10_M           Heart                  LV
## 127     MAA000919        3_10_M           Heart                  LA
## 128     MAA000920        3_11_M           Heart                  LA
## 129     MAA000936         3_9_M           Heart                  RA
## 130       B000633        3_56_F           Heart                  RV
## 131       B000634        3_56_F           Heart                  LA
## 132       B000636        3_56_F           Heart                  LV
## 133       B002010        3_39_F           Heart                  RA
## 134       B002011        3_38_F           Heart                  RA
## 135       B002421        3_39_F           Heart                  LV
## 136       B002423        3_39_F           Heart                  RV
## 137       B002427        3_39_F           Heart                  LA
## 138       B002428        3_38_F           Heart                  LA
## 139       B002429        3_38_F           Heart                  LV
## 140       B002430        3_38_F           Heart               Aorta
## 141       B002431        3_39_F           Heart               Aorta
## 142       B000412        3_56_F           Heart                  RA
## 143     MAA100037 3_10_M/3_11_M           Heart                   ?
## 144     MAA100096 3_38_F/3_39_F           Heart                   ?
## 145     MAA100097 3_38_F/3_39_F           Heart               LA/RA
## 146     MAA000545         3_8_M          Kidney                <NA>
## 147     MAA000752        3_10_M          Kidney                <NA>
## 148     MAA000801        3_11_M          Kidney                <NA>
## 149     MAA000922         3_9_M          Kidney                <NA>
## 150       B001717        3_38_F          Kidney                <NA>
## 151       B002775        3_39_F          Kidney                <NA>
## 152     MAA000377         3_9_M           Liver     Non-hepatocytes
## 153     MAA000907        3_11_M           Liver     Non-hepatocytes
## 154     MAA100039        3_11_M           Liver         Hepatocytes
## 155     MAA100040        3_11_M           Liver         Hepatocytes
## 156     MAA100138        3_56_F           Liver         Hepatocytes
## 157     MAA100140        3_57_F           Liver         Hepatocytes
## 158     MAA100041         3_9_M           Liver         Hepatocytes
## 159     MAA100042         3_9_M           Liver         Hepatocytes
## 160     MAA000526         3_9_M            Lung                <NA>
## 161     MAA000530         3_8_M            Lung                <NA>
## 162     MAA000839        3_11_M            Lung                <NA>
## 163     MAA000891        3_10_M            Lung                <NA>
## 164     MAA001847        3_39_F            Lung               EPCAM
## 165     MAA001889        3_38_F            Lung               EPCAM
## 166     MAA001892        3_38_F            Lung           Endomucin
## 167       D043522        3_39_F            Lung           Endomucin
## 168       B002432        3_38_F         Mammary       Mammary_Gland
## 169       B002433        3_38_F         Mammary       Mammary_Gland
## 170       B002435        3_39_F         Mammary       Mammary_Gland
## 171       B002436        3_39_F         Mammary       Mammary_Gland
## 172       B002437        3_39_F         Mammary       Mammary_Gland
## 173       B002438        3_38_F         Mammary       Mammary_Gland
## 174       B000166        3_56_F         Mammary       Mammary_Gland
## 175       B000167        3_56_F         Mammary       Mammary_Gland
## 176       B000168        3_57_F         Mammary       Mammary_Gland
## 177       B002309        3_57_F         Mammary       Mammary_Gland
## 178       D042044         3_9_M          Marrow             B-cells
## 179       D042467         3_9_M          Marrow             T-cells
## 180       D041912         3_8_M          Marrow        Granulocytes
## 181       D042193         3_8_M          Marrow             T-cells
## 182       D042479         3_8_M          Marrow             B-cells
## 183     MAA000409        3_10_M          Marrow        Granulocytes
## 184     MAA000596         3_9_M          Marrow                 KLS
## 185     MAA000600         3_8_M          Marrow                 KLS
## 186     MAA000639         3_9_M          Marrow        Granulocytes
## 187     MAA000652        3_10_M          Marrow             B-cells
## 188     MAA000844        3_10_M          Marrow             T-cells
## 189     MAA000848        3_10_M          Marrow                 KLS
## 190       D045139        3_39_F          Marrow        Granulocytes
## 191       D045140        3_38_F          Marrow        Granulocytes
## 192     MAA001842        3_38_F          Marrow             T-cells
## 193     MAA001844        3_38_F          Marrow                 KLS
## 194     MAA001883        3_39_F          Marrow             B-cells
## 195     MAA001884        3_38_F          Marrow             B-cells
## 196     MAA001887        3_39_F          Marrow                 KLS
## 197     MAA001888        3_39_F          Marrow             T-cells
## 198       D042103        3_11_M          Muscle ForelimbandHindlimb
## 199       D042105        3_11_M          Muscle           Diaphragm
## 200       D042180        3_10_M          Muscle           Diaphragm
## 201       D042185         3_9_M          Muscle ForelimbandHindlimb
## 202       D042186         3_8_M          Muscle ForelimbandHindlimb
## 203     MAA001454        3_38_F          Muscle           Diaphragm
## 204       B002762        3_39_F          Muscle           Diaphragm
## 205       B002765        3_38_F          Muscle ForelimbandHindlimb
## 206       B002769        3_39_F          Muscle ForelimbandHindlimb
## 207       D042473        3_10_M          Muscle ForelimbandHindlimb
## 208     MAA000574         3_8_M        Pancreas            Exocrine
## 209     MAA000577         3_8_M        Pancreas           Endocrine
## 210     MAA000884        3_10_M        Pancreas           Endocrine
## 211     MAA000910        3_10_M        Pancreas            Exocrine
## 212     MAA001857        3_38_F        Pancreas           Endocrine
## 213     MAA001861        3_39_F        Pancreas            Exocrine
## 214     MAA001862        3_39_F        Pancreas           Endocrine
## 215     MAA001868        3_38_F        Pancreas            Exocrine
## 216     MAA000427        3_11_M            Skin              Anagen
## 217     MAA000435        3_10_M            Skin              Anagen
## 218     MAA000549         3_8_M            Skin              Anagen
## 219     MAA000597         3_9_M            Skin              Anagen
## 220     MAA000614        3_10_M            Skin                <NA>
## 221     MAA000927         3_9_M            Skin                <NA>
## 222     MAA000938         3_8_M            Skin                <NA>
## 223       B003283        3_38_F            Skin                <NA>
## 224       B000126        3_39_F            Skin                <NA>
## 225     MAA000508         3_9_M          Spleen                <NA>
## 226     MAA000559         3_8_M          Spleen                <NA>
## 227     MAA000776        3_10_M          Spleen                <NA>
## 228     MAA000779        3_11_M          Spleen                <NA>
## 229       B000971        3_39_F          Spleen                <NA>
## 230       B001750        3_38_F          Spleen                <NA>
## 231     MAA000607         3_9_M          Thymus                <NA>
## 232     MAA000609         3_8_M          Thymus                <NA>
## 233     MAA000880        3_11_M          Thymus                <NA>
## 234       B000827        3_38_F          Thymus         Flowthrough
## 235       B003284        3_39_F          Thymus          Epithelium
## 236       B003285        3_38_F          Thymus          Epithelium
## 237       B003291        3_39_F          Thymus         Flowthrough
## 238       D041894         3_9_M          Tongue                <NA>
## 239       D041904        3_10_M          Tongue                <NA>
## 240       D041911         3_8_M          Tongue                <NA>
## 241       B002777        3_39_F          Tongue                <NA>
## 242       B002780        3_38_F          Tongue                <NA>
## 243       D042474         3_8_M         Trachea                <NA>
## 244       D042475         3_9_M         Trachea                <NA>
## 245     MAA000496        3_10_M         Trachea                <NA>
## 246     MAA001865        3_38_F         Trachea                <NA>
## 247     MAA001867        3_39_F         Trachea                <NA>
##                                           FACS.selection mouse.sex
## 1                                               Multiple         M
## 2                                               Multiple         M
## 3                                               Multiple         M
## 4                                               Multiple         F
## 5                                               Multiple         F
## 6                                               Multiple         F
## 7                                                Neurons         M
## 8                                                Neurons         M
## 9                                                   <NA>         M
## 10                                               Neurons         M
## 11                                               Neurons         M
## 12                                               Neurons         M
## 13                                               Neurons         M
## 14                                             Microglia         M
## 15                                             Microglia         M
## 16                                             Microglia         M
## 17                                             Microglia         M
## 18                                               Neurons         M
## 19                                               Neurons         M
## 20                                             Microglia         M
## 21                                             Microglia         M
## 22                                             Microglia         M
## 23                                             Microglia         M
## 24                                             Microglia         M
## 25                                             Microglia         M
## 26                                             Microglia         M
## 27                                               Neurons         M
## 28                                             Microglia         M
## 29                                               Neurons         M
## 30                                              Multiple         M
## 31                                               Neurons         M
## 32                                               Neurons         M
## 33                                               Neurons         M
## 34                                               Neurons         M
## 35                                               Neurons         M
## 36                                               Neurons         M
## 37                                               Neurons         M
## 38                                               Neurons         M
## 39                                               Neurons         M
## 40                                               Neurons         M
## 41                                               Neurons         M
## 42                                               Neurons         M
## 43                                               Neurons         M
## 44                                               Neurons         M
## 45                                               Neurons         M
## 46                                               Neurons         F
## 47                                               Neurons         F
## 48                                               Neurons         F
## 49                                               Neurons         F
## 50                                              Multiple         F
## 51                                               Neurons         F
## 52                                               Neurons         F
## 53                                               Neurons         F
## 54                                             Microglia         F
## 55                                             Microglia         F
## 56                                               Neurons         F
## 57                                               Neurons         F
## 58                                               Neurons         F
## 59                                             Microglia         F
## 60                                             Microglia         F
## 61                                             Microglia         F
## 62                                               Neurons         F
## 63                                             Microglia         F
## 64                                               Neurons         F
## 65                                             Microglia         F
## 66                                               Neurons         F
## 67                                               Neurons         F
## 68                                             Microglia         F
## 69                                              Multiple         M
## 70                                              Multiple         M
## 71                                              Multiple         M
## 72                                              Multiple         M
## 73                                              Multiple         M
## 74                                              Multiple         M
## 75                                              Multiple         M
## 76                                              Multiple         F
## 77                                              Multiple         F
## 78                                              Multiple         F
## 79                                              Multiple         F
## 80                                              Multiple         F
## 81                                              Multiple         F
## 82                                              Multiple         M
## 83                                              Multiple         M
## 84                                              Multiple         M
## 85                                              Multiple         M
## 86                                              Multiple         M
## 87                                              Multiple         M
## 88                                              Multiple         M
## 89                                              Multiple         M
## 90                                              Multiple         M
## 91                                              Multiple         M
## 92                                              Multiple         M
## 93                                              Multiple         M
## 94                                              Multiple         M
## 95                                              Multiple         M
## 96                                              Multiple         M
## 97                                              Multiple         M
## 98                                              Multiple         F
## 99                                              Multiple         F
## 100                                             Multiple         F
## 101                                             Multiple         F
## 102                                             Multiple         F
## 103                                             Multiple         F
## 104                                             Multiple         F
## 105                                             Multiple         F
## 106                                             Multiple         F
## 107                                             Multiple         F
## 108                                             Multiple         F
## 109                                             Multiple         F
## 110                                               Viable         M
## 111                                               Viable         M
## 112                                               Viable         M
## 113                                               Viable         M
## 114                                               Viable         M
## 115                                               Viable         M
## 116                                               Viable         M
## 117                                               Viable         M
## 118                                               Viable         M
## 119                                               Viable         M
## 120                                               Viable         M
## 121                                               Viable         M
## 122                                               Viable         M
## 123                                               Viable         M
## 124                                               Viable         M
## 125                                               Viable         M
## 126                                               Viable         M
## 127                                               Viable         M
## 128                                               Viable         M
## 129                                               Viable         M
## 130                                               Viable         F
## 131                                               Viable         F
## 132                                               Viable         F
## 133                                               Viable         F
## 134                                               Viable         F
## 135                                               Viable         F
## 136                                               Viable         F
## 137                                               Viable         F
## 138                                               Viable         F
## 139                                               Viable         F
## 140                                               Viable         F
## 141                                               Viable         F
## 142                                               Viable         F
## 143                                       cardiomyocytes         M
## 144                                       cardiomyocytes         F
## 145                                       cardiomyocytes         F
## 146                                               Viable         M
## 147                                               Viable         M
## 148                                               Viable         M
## 149                                               Viable         M
## 150                                               Viable         F
## 151                                               Viable         F
## 152                                               Viable         M
## 153                                               Viable         M
## 154                                                 <NA>         M
## 155                                                 <NA>         M
## 156                                                 <NA>         F
## 157                                                 <NA>         F
## 158                                                 <NA>         M
## 159                                                 <NA>         M
## 160                                             Multiple         M
## 161                                             Multiple         M
## 162                                             Multiple         M
## 163                                             Multiple         M
## 164                                             Multiple         F
## 165                                             Multiple         F
## 166                                             Multiple         F
## 167                                             Multiple         F
## 168                                             Multiple         F
## 169                                             Multiple         F
## 170                                             Multiple         F
## 171                                             Multiple         F
## 172                                             Multiple         F
## 173                                             Multiple         F
## 174                                             Multiple         F
## 175                                             Multiple         F
## 176                                             Multiple         F
## 177                                             Multiple         F
## 178                                             Multiple         M
## 179                                             Multiple         M
## 180                                             Multiple         M
## 181                                             Multiple         M
## 182                                             Multiple         M
## 183                                             Multiple         M
## 184                                             Multiple         M
## 185                                             Multiple         M
## 186                                             Multiple         M
## 187                                             Multiple         M
## 188                                             Multiple         M
## 189                                             Multiple         M
## 190                                             Multiple         F
## 191                                             Multiple         F
## 192                                             Multiple         F
## 193                                             Multiple         F
## 194                                             Multiple         F
## 195                                             Multiple         F
## 196                                             Multiple         F
## 197                                             Multiple         F
## 198                                             Multiple         M
## 199                                             Multiple         M
## 200                                             Multiple         M
## 201                                             Multiple         M
## 202                                             Multiple         M
## 203 CD31+; CD45+; CD31-CD45-Sca1+; CD31-CD45-Sca1-VCAM1+         F
## 204 CD31+; CD45+; CD31-CD45-Sca1+; CD31-CD45-Sca1-VCAM1+         F
## 205 CD31+; CD45+; CD31-CD45-Sca1+; CD31-CD45-Sca1-VCAM1+         F
## 206 CD31+; CD45+; CD31-CD45-Sca1+; CD31-CD45-Sca1-VCAM1+         F
## 207                                             Multiple         M
## 208                                               Viable         M
## 209                                               Viable         M
## 210                                               Viable         M
## 211                                               Viable         M
## 212                                               Viable         F
## 213                                               Viable         F
## 214                                               Viable         F
## 215                                               Viable         F
## 216                                             Multiple         M
## 217                                             Multiple         M
## 218                                             Multiple         M
## 219                                             Multiple         M
## 220                                             Multiple         M
## 221                                             Multiple         M
## 222                                             Multiple         M
## 223                                             Multiple         F
## 224                                             Multiple         F
## 225                                               Viable         M
## 226                                               Viable         M
## 227                                               Viable         M
## 228                                               Viable         M
## 229                                               Viable         F
## 230                                               Viable         F
## 231                                             Multiple         M
## 232                                             Multiple         M
## 233                                             Multiple         M
## 234                                             Multiple         F
## 235                                             Multiple         F
## 236                                             Multiple         F
## 237                                             Multiple         F
## 238                                             Multiple         M
## 239                                             Multiple         M
## 240                                             Multiple         M
## 241                                             Multiple         F
## 242                                             Multiple         F
## 243                                             Multiple         M
## 244                                             Multiple         M
## 245                                             Multiple         M
## 246                                             Multiple         F
## 247                                             Multiple         F

Subset the metadata on the tissue.

tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_plates
##   plate.barcode tissue       subtissue mouse.sex
## 1     MAA000377  Liver Non-hepatocytes         M
## 2     MAA000907  Liver Non-hepatocytes         M
## 3     MAA100039  Liver     Hepatocytes         M
## 4     MAA100040  Liver     Hepatocytes         M
## 5     MAA100138  Liver     Hepatocytes         F
## 6     MAA100140  Liver     Hepatocytes         F
## 7     MAA100041  Liver     Hepatocytes         M
## 8     MAA100042  Liver     Hepatocytes         M

Load the read count data.

#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', 'facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))

raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)
##               F18.MAA000377.3_9_M.1.1 J20.MAA000377.3_9_M.1.1
## 0610005C13Rik                       0                       0
## 0610007C21Rik                       0                       0
## 0610007L01Rik                      23                       0
## 0610007N19Rik                       0                       0
## 0610007P08Rik                       0                       0
##               F19.MAA000377.3_9_M.1.1 J21.MAA000377.3_9_M.1.1
## 0610005C13Rik                       0                       0
## 0610007C21Rik                       0                       0
## 0610007L01Rik                       0                       0
## 0610007N19Rik                       0                       0
## 0610007P08Rik                       0                       0
##               F20.MAA000377.3_9_M.1.1
## 0610005C13Rik                       0
## 0610007C21Rik                       2
## 0610007L01Rik                       0
## 0610007N19Rik                       0
## 0610007P08Rik                       0

Make a vector of plate barcodes for each cell

plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)
## [[1]]
## [1] "MAA000377"
## 
## [[2]]
## [1] "MAA000377"
## 
## [[3]]
## [1] "MAA000377"
## 
## [[4]]
## [1] "MAA000377"
## 
## [[5]]
## [1] "MAA000377"
## 
## [[6]]
## [1] "MAA000377"

Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to “expand” the per-plate metadata to be per-cell.

barcode.df = t.data.frame(as.data.frame(plate.barcodes))

rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df)
##                         plate.barcode
## F18.MAA000377.3_9_M.1.1 "MAA000377"  
## J20.MAA000377.3_9_M.1.1 "MAA000377"  
## F19.MAA000377.3_9_M.1.1 "MAA000377"  
## J21.MAA000377.3_9_M.1.1 "MAA000377"  
## F20.MAA000377.3_9_M.1.1 "MAA000377"  
## J22.MAA000377.3_9_M.1.1 "MAA000377"
rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames

# Sort cells by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
corner(meta.data)
##                         plate.barcode mouse.id tissue       subtissue
## F18.MAA000377.3_9_M.1.1     MAA000377    3_9_M  Liver Non-hepatocytes
## J20.MAA000377.3_9_M.1.1     MAA000377    3_9_M  Liver Non-hepatocytes
## F19.MAA000377.3_9_M.1.1     MAA000377    3_9_M  Liver Non-hepatocytes
## J21.MAA000377.3_9_M.1.1     MAA000377    3_9_M  Liver Non-hepatocytes
## F20.MAA000377.3_9_M.1.1     MAA000377    3_9_M  Liver Non-hepatocytes
##                         FACS.selection
## F18.MAA000377.3_9_M.1.1         Viable
## J20.MAA000377.3_9_M.1.1         Viable
## F19.MAA000377.3_9_M.1.1         Viable
## J21.MAA000377.3_9_M.1.1         Viable
## F20.MAA000377.3_9_M.1.1         Viable
raw.data = raw.data[, rownames(meta.data)]
corner(raw.data)
##               F18.MAA000377.3_9_M.1.1 J20.MAA000377.3_9_M.1.1
## 0610005C13Rik                       0                       0
## 0610007C21Rik                       0                       0
## 0610007L01Rik                      23                       0
## 0610007N19Rik                       0                       0
## 0610007P08Rik                       0                       0
##               F19.MAA000377.3_9_M.1.1 J21.MAA000377.3_9_M.1.1
## 0610005C13Rik                       0                       0
## 0610007C21Rik                       0                       0
## 0610007L01Rik                       0                       0
## 0610007N19Rik                       0                       0
## 0610007P08Rik                       0                       0
##               F20.MAA000377.3_9_M.1.1
## 0610005C13Rik                       0
## 0610007C21Rik                       2
## 0610007L01Rik                       0
## 0610007N19Rik                       0
## 0610007P08Rik                       0

Process the raw data and load it into the Seurat object.

# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)

tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'

# Create metadata columns for annotations and subannotations
tiss@meta.data[,'annotation'] <- NA
tiss@meta.data[,'subannotation'] <- NA

Calculate percent ribosomal genes.

ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")

A sanity check: genes per cell vs reads per cell.

### !clusters of hep. with nGene 6000 - 8000, not present in 10x run from same mice
GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)

Filter out cells with few reads and few genes.

tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"), 
    low.thresholds = c(500, 50000), high.thresholds = c(25000, 5000000))

Normalize the data, then regress out correlation with total reads

tiss <- NormalizeData(object = tiss)
tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))
## [1] "Regressing out nReads"       "Regressing out percent.ribo"
## [3] "Regressing out Rn45s"       
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## [1] "Scaling data matrix"
## 
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tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)

Run Principal Component Analysis.

tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

PCElbowPlot(object = tiss)

Choose the number of principal components to use.

# Set number of principal components. 
n.pcs = 10

The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

# Set resolution 
res.used <- 0.3

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)

To visualize

# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=15)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)

tiss = BuildClusterTree(tiss)
## [1] "Finished averaging RNA for cluster 0"
## [1] "Finished averaging RNA for cluster 1"
## [1] "Finished averaging RNA for cluster 2"
## [1] "Finished averaging RNA for cluster 3"
## [1] "Finished averaging RNA for cluster 4"
## [1] "Finished averaging RNA for cluster 5"

table(tiss@ident)
## 
##   0   1   2   3   4   5 
## 296 196 103  51  35  29

Check expression of genes of interset.

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

VlnPlot(object = tiss, features.plot = c("Klrb1a", "Klrb1c", "Cd4", "Cd8a"), use.raw = TRUE, nCol = 2)

GenePlot(tiss, 'Cxcr6', 'Nkg7', use.raw = T)

GenePlot(tiss, 'Cxcr6', 'Cd8a', use.raw = T)

GenePlot(tiss, 'Cyp2e1', 'Cyp2f2', use.raw = T)

How big are the clusters?

table(tiss@ident)
## 
##   0   1   2   3   4   5 
## 296 196 103  51  35  29

Which markers identify a specific cluster?

#clust.markers <- FindMarkers(object = tiss, ident.1 = 6, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
#print(x = head(x= clust.markers, n = 20))
clust.markers <- FindMarkers(object = tiss, ident.1 = 4, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
print(x = head(x= clust.markers, n = 20))
##                 p_val  avg_diff pct.1 pct.2
## Zap70    1.482652e-66 1.8059677 0.943 0.308
## Ms4a4b   6.730391e-56 2.8092692 0.971 0.030
## Nkg7     1.709649e-54 3.4867188 0.914 0.025
## Selplg   1.064103e-53 2.3380461 1.000 0.431
## Il2rb    1.458926e-53 2.6275301 0.971 0.010
## Lck      5.829937e-52 2.7934577 1.000 0.071
## Cd3g     2.954488e-51 2.3884440 0.886 0.024
## Ccl5     2.568174e-49 2.9250136 0.943 0.037
## Skap1    9.844417e-49 2.0781412 0.971 0.024
## Ctsw     3.158630e-47 2.4765132 0.914 0.018
## Cxcr6    1.727933e-46 3.4080946 0.829 0.006
## Coro1a   4.285152e-46 2.4679537 1.000 0.271
## Tmsb10   5.952440e-45 0.8141295 1.000 0.261
## Gimap3   1.042351e-44 2.4229643 1.000 0.058
## Cd3d     5.170914e-44 1.8587218 0.886 0.027
## B4galnt1 9.517737e-44 1.9079277 0.943 0.385
## Arsb     7.171493e-43 1.0421826 0.543 0.299
## Cd3e     2.377490e-42 0.7760406 0.886 0.012
## Id2      4.534231e-42 1.8714379 0.971 0.721
## Abcb1a   9.804462e-42 0.5947887 0.600 0.043

You can also compute all markers for all clusters at once. This may take some time.

tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

Display the top markers you computed above.

top_genes <- tiss.markers %>% group_by(cluster) %>% top_n(50, avg_diff)
print(top_genes)
## # A tibble: 300 x 6
## # Groups:   cluster [6]
##            p_val avg_diff pct.1 pct.2 cluster  gene
##            <dbl>    <dbl> <dbl> <dbl>  <fctr> <chr>
##  1 1.716084e-106 1.295394 0.956 0.459       0 Aldob
##  2 5.020611e-101 1.466199 0.973 0.529       0 Fabp1
##  3 6.195928e-100 1.791094 0.976 0.754       0  Mup3
##  4  1.406955e-96 1.302912 0.986 0.700       0 Apoa2
##  5  9.543387e-95 1.402257 0.976 0.635       0 Apoc1
##  6  2.036623e-89 1.944940 0.932 0.780       0 Gstp1
##  7  2.122724e-89 1.104824 0.946 0.425       0  Adh1
##  8  1.670452e-88 1.521682 0.956 0.771       0  Scp2
##  9  4.986071e-88 2.698246 0.659 0.592       0  Saa1
## 10  2.866670e-85 1.581322 0.949 0.437       0  Bhmt
## # ... with 290 more rows
# find all markers distinguishing FEMALE vs MALE
#cluster_sex.markers <- FindMarkers(object = tiss, ident.1 = c(0), ident.2 = c(2,5), 
#    min.pct = 0.25)
#print(x = head(x = cluster_sex.markers, n = 50))
#write.csv(x = head(x = cluster_sex.markers, n = 20), "clus-marker-SEX.csv")

Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5)
annotation <- c("hepatocyte",   "endothelial cell of hepatic sinusoid", "hepatocyte", "Kupffer cell", "natural killer cell", 
                "B cell")
cell_ontology_id <- c("CL:0000182",   "CL:1000398", "CL:0000182", "CL:0000091", "CL:0000623", 
                "CL:0000236")


tiss@meta.data[,'annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = annotation)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'annotation'] <- as.character(tiss@meta.data$annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)

TSNEPlot(object = tiss, do.label = TRUE, pt.size = 1, group.by='annotation')

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")

TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")

Print a table showing the count of cells in each identity category from each plate.

table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
##    
##     MAA000377 MAA000907 MAA100039 MAA100040 MAA100041 MAA100042 MAA100138
##   0        29        52        65        36        21        25        34
##   1        21       162         0         0         0        13         0
##   2         5         4         0         0         1         0        48
##   3        12        39         0         0         0         0         0
##   4         7        28         0         0         0         0         0
##   5         6        23         0         0         0         0         0
##    
##     MAA100140
##   0        34
##   1         0
##   2        45
##   3         0
##   4         0
##   5         0

Save the Robject for later

When you save the annotated tissue, please give it a name.

filename = here('00_data_ingest', 'tissue_seurat_robj', 
                     paste0(tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
## [1] "/Users/olgabot/code/tabula-muris/00_data_ingest/tissue_seurat_robj/Liver_seurat_tiss.Robj"
save(tiss, file=filename)
# To reload a saved object
# filename = here('00_data_ingest', 'tissue_seurat_robj', 
#                      paste0(tissue_of_interest, "_seurat_tiss.Robj"))
# load(file=filename)

Export the final metadata

So that Biohub can easily combine all your annotations, please export them as a simple csv.

head(tiss@meta.data)
##                         nGene nReads orig.ident plate.barcode mouse.id
## F18.MAA000377.3_9_M.1.1  2349 341976      Liver     MAA000377    3_9_M
## J20.MAA000377.3_9_M.1.1  2655 358887      Liver     MAA000377    3_9_M
## F19.MAA000377.3_9_M.1.1  1605 378745      Liver     MAA000377    3_9_M
## J21.MAA000377.3_9_M.1.1  1538 169724      Liver     MAA000377    3_9_M
## F20.MAA000377.3_9_M.1.1  2625 371694      Liver     MAA000377    3_9_M
## J22.MAA000377.3_9_M.1.1  1720 152241      Liver     MAA000377    3_9_M
##                         tissue       subtissue FACS.selection mouse.sex
## F18.MAA000377.3_9_M.1.1  Liver Non-hepatocytes         Viable         M
## J20.MAA000377.3_9_M.1.1  Liver Non-hepatocytes         Viable         M
## F19.MAA000377.3_9_M.1.1  Liver Non-hepatocytes         Viable         M
## J21.MAA000377.3_9_M.1.1  Liver Non-hepatocytes         Viable         M
## F20.MAA000377.3_9_M.1.1  Liver Non-hepatocytes         Viable         M
## J22.MAA000377.3_9_M.1.1  Liver Non-hepatocytes         Viable         M
##                         percent.ercc                           annotation
## F18.MAA000377.3_9_M.1.1   0.03546739                  natural killer cell
## J20.MAA000377.3_9_M.1.1   0.03315256                  natural killer cell
## F19.MAA000377.3_9_M.1.1   0.03014714                           hepatocyte
## J21.MAA000377.3_9_M.1.1   0.07767217                         Kupffer cell
## F20.MAA000377.3_9_M.1.1   0.03400151 endothelial cell of hepatic sinusoid
## J22.MAA000377.3_9_M.1.1   0.06422069                         Kupffer cell
##                         subannotation percent.ribo res.0.3 cluster.ids
## F18.MAA000377.3_9_M.1.1            NA  0.029622473       4           4
## J20.MAA000377.3_9_M.1.1            NA  0.024196700       4           4
## F19.MAA000377.3_9_M.1.1            NA  0.007964172       0           0
## J21.MAA000377.3_9_M.1.1            NA  0.023341161       3           3
## F20.MAA000377.3_9_M.1.1            NA  0.010819902       1           1
## J22.MAA000377.3_9_M.1.1            NA  0.020444509       3           3
##                         cell_ontology_id
## F18.MAA000377.3_9_M.1.1       CL:0000623
## J20.MAA000377.3_9_M.1.1       CL:0000623
## F19.MAA000377.3_9_M.1.1       CL:0000182
## J21.MAA000377.3_9_M.1.1       CL:0000091
## F20.MAA000377.3_9_M.1.1       CL:1000398
## J22.MAA000377.3_9_M.1.1       CL:0000091
filename = here('00_data_ingest', 'tissue_annotation_csv', 
                     paste0(tissue_of_interest, "_annotation.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','annotation','cell_ontology_id')], file=filename)